![]() |
MeLOn
|
Functions | |
json_X (end) | |
fwrite (fid, json) | |
fclose (fid) | |
Variables | |
sample_lb = min(X) | |
Compute lower bound of input data | sample_ub = max(X) |
Compute upper bound of input | data [nX, DX] = size(X) |
scale hyperparameters from log | ell = exp(Opt.GP.hyp.cov(1:DX)) |
sf2 = exp(2*Opt.GP.hyp.cov(DX+1)) | |
data | nY = nY |
data | DX = DX |
data | DY = DY |
data | matern = Opt.GP.matern |
data | meanfunction = 0 |
data | meanOfOutput = meanOfOutput |
data | stdOfOutput = stdOfOutput |
data | inputLowerBound = {sample_lb} |
data | inputUpperBound = {sample_ub} |
data | problemLowerBound = {lb} |
data | problemUpperBound = {ub} |
fix for column vector data | X = {'X_dummy'} |
json_X = sprintf('[%f],',xScaled) | |
end data | Y = yScaled |
data | K = Opt.GP.K |
data | invK = Opt.GP.invK |
json = jsonencode(data) | |
end | fid = fopen(filename, 'w') |
path = fullfile(pwd, filename) | |
fclose | ( | fid | ) |
json_X | ( | end | ) |
if DX = DX |
data DY = DY |
else data ell = exp(Opt.GP.hyp.cov(1:DX)) |
end fid = fopen(filename, 'w') |
data invK = Opt.GP.invK |
json_X = sprintf('[%f],',xScaled) |
data K = Opt.GP.K |
data matern = Opt.GP.matern |
data meanfunction = 0 |
data meanOfOutput = meanOfOutput |
data nY = nY |
path = fullfile(pwd, filename) |
sample_lb = min(X) |
data sf2 = exp(2*Opt.GP.hyp.cov(DX+1)) |
data stdOfOutput = stdOfOutput |
end data Y = yScaled |